docs: def _distarray_missing(self, xc, xd, cdiffs):
          """Distance array calculation for data with missing values"""
          cindices = []
          dindices = []
          # Get Boolean mask locating missing values for continuous and discrete features separately. These correspond to xc and xd respectively.
          for i in range(self._datalen):
              cindices.append(np.where(np.isnan(xc[i]))[0])
              dindices.append(np.where(np.isnan(xd[i]))[0])
  
          if self.n_jobs != 1:
              dist_array = Parallel(n_jobs=self.n_jobs)(delayed(get_row_missing)(
                  xc, xd, cdiffs, index, cindices, dindices) for index in range(self._datalen))
          else:
              # For each instance calculate distance from all other instances (in non-redundant manner) (i.e. computes triangle, and puts zeros in for rest to form square).
              dist_array = [get_row_missing(xc, xd, cdiffs, index, cindices, dindices)
                            for index in range(self._datalen)]
  
          return np.array(dist_array)